Device and method for determining spinal cord stimulation efficacy

ABSTRACT

Device and method for determining an efficacy of chronic pain treatment including providing a first set of at least one stimulus to a subject, obtaining first measurements of at least two physiological parameters in response to the first set of at least one stimulus, providing chronic pain treatment to the subject, providing a second set of at least one stimulus to the subject, obtaining second measurements of the at least two physiological parameters in response to the second set of at least one stimulus; and determining an efficacy of the chronic pain treatment by applying a classification algorithm on the first and second measurements of the at least two physiological parameters.

RELATED APPLICATION DATA

This application is the U.S. National Stage of International ApplicationNo. PCT/IL2014/050851 filed Sep. 29, 2014, which claims the benefit ofU.S. Provisional Patent Application No. 61/884,089 filed Sep. 29, 2013.Each of the foregoing applications is hereby incorporated by referencein its entirety for all purposes.

TECHNICAL FIELD

The present disclosure relates generally to the field of medical devicesand, more particularly, electrical nerve stimulators.

BACKGROUND

Spinal cord stimulation (SCS) is a widely used treatment for a number ofpain conditions and is frequently considered a suitable pain managementoption when conservative or less invasive techniques have proven to beineffective.

Spinal Cord Stimulation (SCS), as its name suggests, provides nervestimulation to the spinal cord by introducing electrical pulses in thedorsal aspect of the spinal cord. This form of stimulation is believedto interfere with the transmission of pain signals to the brain and toreplace them with a more pleasant sensation called paresthesia. Spinalcord stimulation (SCS), in the simplest form, consists of stimulatingelectrodes, implanted in the epidural space, an electrical pulsegenerator, implanted in the lower abdominal area or gluteal region,conducting wires connecting the electrodes to the generator, and aremote control of the generator.

While research on SCS is in its infancy, it is clear that there is asubstantial variation in the degree of benefit of SCS between patients.To date before proceeding with permanent SCS implantation, a stimulationtrial is performed. The SCS trial procedure is a minimally invasiveprocedure (similar to placing an epidural catheter). The trial allowsthe patients to evaluate the SCS analgesic activity in their everydaysurroundings. The trial can last for between hours and up to a week,whereafter efficacy is subjectively determined based on patient's senseof pain relief. The criteria for a successful trial is at least a 50%reduction in pain, a decrease in analgesic intake and significantfunctional improvement. Most patients who are not sure that the spinalcord stimulator trial helped will not get a permanent spinal cordstimulator.

Despite the strict criteria as many as 20-30% of patients fail toexperience pain relief following permanent SCS implantation. Oppositely,patients who fail to acknowledge a real reduction in pain relief may notget a permanent SCS implantation, even though they could have benefittherefrom.

SUMMARY

Aspects of the disclosure, in some embodiments thereof, relate to deviceand method for assessing chronic pain treatment efficacy. The methoddisclosed herein may include providing a first set of stimuli to asubject; obtaining first measurements of at least two physiologicalparameters in response to the first set of stimuli; providing a chronicpain treatment to the subject; providing a second set of stimuli to thesubject; obtaining second measurements of the at least two physiologicalparameters in response to the second set of stimuli; and determining anefficacy of the chronic pain treatment by applying a classificationalgorithm on the first and second measurements of the at least twophysiological parameters, obtained in response to the first and secondset of stimuli, respectively.

Subjects suffering from chronic pain do so during a prolonged period oftime, consequently affecting brain structure and function. As a result,recognizing pain relief in response to short term treatments may bedifficult for a subject suffering from chronic pain. The difficulty ofassessing pain relief for patients suffering from chronic pain isfurther augmented by the fact that psychological factors may affect theassessment. The psychological factors may, on the one hand, be factorsleading to exaggeration of real efficacy, for example due to keenness ofpain relief; and on the other hand, be factors leading to understatementof real efficacy, for example due to fear of surgery, such asimplantation of a permanent SCS device. In effect, as many as 20-30% ofpatients fail to experience pain relief following permanent SCSimplantation, despite pledging pain relief during a SCS trial, andoppositely real benefiters may be overseen due to a failure to recognizethe benefit of the SCS trial.

Advantageously, the device and method, disclosed herein, may enableobjective assessment of chronic pain treatment efficacy in a subject.The method may be of particular benefit when assessing the efficacy of achronic pain treatment trials, such as implantation of a temporary SCSdevice prior to permanent SCS implantation.

Similarly, desensitization and/or resistance to the chronic paintreatment may be developed during time. As desensitization and/orresistance may develop gradually it may initially go unrecognized by thepatient until a certain pain threshold is reached, thereby causing asetback in the treatment of the chronic pain.

Advantageously, the device and method disclosed herein, enableperiodical assessment of the efficacy of a chronic pain treatment in asubject, thereby identifying changes in efficacy which may requireadjustments and/or changes in treatment regimen and/or type; thispreferably prior to reversion of pain and treatment setback. Suchassessment may be of particular importance prior to surgical procedures,such as the procedure required for changing batteries in a permanentlyimplanted SCS device.

The level of pain experienced by the subject suffering from chronic painmay be variable depending on factors such as activity level (whethercurrent activity or aftermaths of a prior activity), body positioning,weather or the like. Advantageously, the device and method, disclosedherein, enables adjustment of SCS operating parameters based onmeasurements of at least one physiological signal, until SCS parametersyielding a highest efficacy are identified. The changes in the SCSparameters are thus based on an objective assessment of SCS efficacy,rather than on changes in the activity of the subject per se, as knownin the art.

According to some embodiments, there is provided a method fordetermining an efficacy of spinal cord stimulation (SCS) treatment in asubject with chronic pain, the method comprising: providing a first setof at least one stimulus to the subject; obtaining first measurements ofat least two physiological parameters in response to the first set of atleast one stimulus; providing SCS treatment to the subject; providing asecond set of at least one stimulus to the subject; obtaining secondmeasurements of the at least two physiological parameters in response tothe second set of at least one stimulus and the SCS treatment; anddetermining an efficacy of the SCS treatment by applying aclassification algorithm on the first and second measurements of the atleast two physiological parameters, obtained in response to the firstand second set of at least one stimulus, respectively.

According to some embodiments, applying the classification algorithmcomprises directly or indirectly comparing the first and secondmeasurements to pre-stored data sets of measurements obtained fromsubjects with known SCS treatment efficacies.

According to some embodiments, determining the efficacy of the SCStreatment is further based on patient demographic data.

According to some embodiments, providing the SCS treatment comprisesproviding a trial SCS treatment. According to some embodiments, thetrial SCS treatment comprises implanting into the subject a temporaryspinal cord stimulator comprising stimulating electrodes only. Accordingto some embodiments, the trial SCS treatment comprises implanting intothe subject a temporary spinal cord stimulator without implanting anelectrical pulse generator.

According to some embodiments, the method further comprises predicting atreatment efficacy of long-term SCS treatment based on the determinedefficacy of the trial SCS treatment.

According to some embodiments, the method further comprises providing atreatment recommendation based on the predicted treatment efficacy.

According to some embodiments, the at least one stimulus is selectedfrom: painful stimulus on non-painful area, painful stimulus on painfularea, non-painful stimulus on non-painful area, non-painful stimulus onpainful area. According to some embodiments, the source of the at leastone stimulus is selected from tetanic stimulus, thermal (heat or cold)stimulus, pressure stimulus, touch (tickle, itch, crude, flutter,pressure) stimulus, electric stimulus, mechanical stimulus,proprioception stimulus, chemical stimulus or combinations thereof.According to some embodiments, the painful stimulus and the non-painfulstimulus is of a different or a same source.

According to some embodiments, the at least two physiological parametersare selected from the group consisting of photoplethysmograph (PPG) Peak(P) amplitude, mean PPG Peak (P) amplitude, standard deviation (std) ofPPG Peak (P) amplitude, Trough (T) amplitude, mean Trough (T) amplitude,std of Trough (T) amplitude; PPG dicrotic notch (N) amplitude, meandicrotic notch (N) amplitude, std of dicrotic notch (N) amplitude, PPGpeak to peak time intervals, PPG peak to peak interval mean, PPG peak topeak interval std; power spectrum of the PPG peak to peak intervals: VLFPower, LF Power and HF Power; PPG envelope-time analysis; PPG envelopespectral analysis; galvanic skin response (GSR) amplitude, GSR meanamplitude, GSR amplitude std; GSR Peak (P) amplitude, mean Peak (P)amplitude and Peak (P) amplitude std; GSR peak to peak time intervals,mean GSR peak to peak time interval; GSR peak to peak time intervalsstd; Phasic EDA: amplitude, mean amplitude and std of amplitude,Temperature amplitude, mean amplitude and std of amplitude; Temp Peak(P) amplitude, mean amplitude and std of amplitude; Temperature peak topeak time intervals, mean and std (variability) of interval; PPG to PPGPulse transit time, ECG to PPG Pulse Transition time; ECG R to R timeintervals, mean and std (variability) of intervals; Power of VLF, LF andHF frequency bands of power spectrum of the ECG R to R intervals (heartrate variability); Upper peak amplitude, mean amplitude and STD ofamplitude; Respiratory rate, mean rate and std rate; Power of thefrequency bands of power spectrum of EMG signal; EMG Power Spectrum Meanfrequency; EMG Power Spectrum Highest Peak Frequency; Power of thealpha, beta, gamma, delta, theta frequency bands of power spectrum ofEEG/FEMG signal; EMG Power Spectrum Mean frequency; EMG Power Spectraledge frequency; Coherence between 2 or more EEG/FEMG channels; frequencyof movement, axis of movement and any combination thereof.

According to some embodiments, the at least two physiological parametersare selected from the group consisting of PPG amplitude, PPG amplitudevariation, pulse rate, pulse rate variability, GSR level, GSRfluctuations or any combination thereof.

According to some embodiments, the at least two physiological parametersare derived from at least one physiological signal selected fromphotoplethysmograph (PPG), galvanic skin response (GSR),electrocardiogram (ECG), blood pressure, respiration, internal bodytemperature, skin temperature, electrooculography (EOG), pupil diameter,electroencephalogram (EEG), frontalis electromyogram (FEMG),electromyography (EMG), electro-gastro-gram (EGG), laser Dopplervelocimetry (LDV), dynamic light scattering (DLS), Near InfraredSpectroscopy (NIRS), partial pressure of carbon dioxide, andaccelerometer readings.

According to some embodiments, the at least two physiological parametersare derived from a photoplethysmograph (PPG) signal and a galvanic skinresponse signal.

According to some embodiments, the method further comprises obtaining atleast three physiological parameters.

According to some embodiments, the chronic pain is selected from FailedBack Surgery Syndrome (FBSS), complex regional pain syndrome (CRPS),Radiculopathy, Peripheral Vascular Disease (PVD), Neuralgia, Neuropathicpain, refractory angina pectoris (RAP), Ischemic pain. According to someembodiments, there is provided a method for calibrating spinal cordstimulation (SCS) treatment in a subject, the method comprising:providing a SCS treatment being characterized by at least one SCSparameter; varying a value of one of the at least one SCS parameteralong a dynamic range thereof; obtaining measurements of at least twophysiological parameters in response to varying the value of the one SCSparameter along the dynamic range thereof; determining an efficacy ofthe SCS treatment along the dynamic range of the varied SCS parameter byapplying a classification algorithm to the at least two physiologicalparameters obtained in response to varying the one SCS parameter alongthe dynamic range thereof; and selecting the value of the one SCSparameter yielding the highest efficacy.

According to some embodiments, the remaining parameters of the at leastone parameters are fixed while varying the one parameter.

According to some embodiments, the at least one SCS parameter comprisestype of stimulation, stimulation frequency, duration, pulse width,intensity, waveform, wave pattern, signal, amplitude, onset timing,delay, treatment length, treatment period, onset delay or anycombination thereof.

According to some embodiments, varying the SCS parameter along thedynamic range comprises making continuous, incremental and/or step wisechanges in the value of the SCS parameter.

According to some embodiments, there is provided a system fordetermining efficacy of a SCS treatment, the system comprising aprocessor configured to obtain a first measurements of at least twophysiological parameters in response to a first set of at least onestimulus; obtain a second measurements of the at least two physiologicalparameters in response to a second set of the at least one stimulus anda SCS treatment; and determine an efficacy of the SCS treatment byapplying a classification algorithm on the first and second measurementsof the at least two physiological parameters, obtained in response tothe first and second set of at least one stimulus.

According to some embodiments, the system further comprises a stimulusevoking device configured to provide at least one stimulus to thesubject.

According to some embodiments, the system further comprises a SCSdevice.

Certain embodiments of the present disclosure may include some, all, ornone of the above advantages. One or more technical advantages may bereadily apparent to those skilled in the art from the figures,descriptions and claims included herein. Moreover, while specificadvantages have been enumerated above, various embodiments may includeall, some or none of the enumerated advantages.

BRIEF DESCRIPTION OF THE DRAWINGS

Some embodiments of the disclosure are described herein with referenceto the accompanying figures. The description, together with the figures,makes apparent to a person having ordinary skill in the art how someembodiments of the disclosure may be practiced. The figures are for thepurpose of illustrative discussion and no attempt is made to showstructural details of an embodiment in more detail than is necessary fora fundamental understanding of the teachings of the disclosure. For thesake of clarity, some objects depicted in the figures are not to scale.

FIG. 1A schematically illustrates a system for determining chronic paintreatment efficacy, according to some embodiment;

FIG. 1B schematically illustrates a system for determining chronic paintreatment efficacy, according to some embodiment;

FIG. 2A is a flowchart of an exemplary method, according to someembodiment;

FIG. 2B is a flowchart of an exemplary method according to someembodiment;

FIG. 3 is a flowchart showing an exemplary method according to someembodiment;

FIG. 4 is a flowchart showing an exemplary method according to someembodiment.

DETAILED DESCRIPTION

In the following description, various aspects of the disclosure will bedescribed. For the purpose of explanation, specific configurations anddetails are set forth in order to provide a thorough understanding ofthe different aspects of the disclosure. However, it will also beapparent to one skilled in the art that the disclosure may be practicedwithout specific details being presented herein. Furthermore, well-knownfeatures may be omitted or simplified in order not to obscure thedisclosure.

It is understood by one of ordinary skill of the art that the order ofthe methods as described should not be construed as sequential steps,and a different sequence of events may be envisaged.

According to some embodiments, there is provided a method fordetermining an efficacy of chronic pain treatment in a subject sufferingfrom chronic pain. According to some embodiments, the method includesproviding a first set of at least one stimulus to the subject; obtainingfirst measurements of at least two physiological parameters in responseto the first set of at least one stimulus; providing a chronic paintreatment to the subject; providing a second set of at least onestimulus to the subject; obtaining second measurements of the at leasttwo physiological parameters in response to the second set of at leastone stimulus; and determining an efficacy of the chronic pain treatmentby applying a classification algorithm on the first and secondmeasurements of the at least two physiological parameters, obtained inresponse to the first and second set of at least one stimulus,respectively.

As used herein, the term “chronic pain” may refer to pain that persistsfor 6 months or longer and typically results from long-standing(chronic) medical conditions or actual or potential damage to the body.A number of symptoms can accompany chronic pain and can even arise as adirect result of the pain. These may include insomnia or poor qualitysleep, irritability, depression and other mood changes, anxiety,fatigue, loss of interest in daily activities and may lead todisability. Chronic pain types can have somatic, visceral or neuropathicorigin.

As referred to herein, the terms “patient” and “subject” mayinterchangeably be used and may relate to a subject suffering fromchronic pain.

As used herein the term “efficacy” with regards to a chronic paintreatment may refer the degree of pain relief obtained due to the painrelief treatment. According to some embodiments, the efficacy may bepresented as scores, index values, percentiles, probabilities or anyother measure configured to intuitively presenting the efficacy of thetreatment. Each possibility is a separate embodiment. According to someembodiment, the efficacy may be indicative of the likelihood of asubject benefitting from a particular chronic pain treatment.

According to some embodiments, the chronic pain treatment may includespinal cord stimulation (SCS). As used herein, the terms “spinal cordstimulation” and “SCS” may refer to neural stimulation by providingelectrical pulses to the dorsal aspect of the spinal cord. According tosome embodiments, the chronic pain treatment may include other types ofneural stimulation for the treatment of chronic pain such as peripheralnerve stimulation, transcutaneous electrical nerve stimulation (TENS),sacral nerve stimulation or deep brain stimulation. Each possibility isa separate embodiment.

Additionally or alternatively, the chronic pain treatment may includeanalgesics medications, various injections such as nerve blocks,epidural injections or trigger point injections, physical treatmentssuch as physiotherapy, acupuncture or more invasive therapies such asablation, radio frequency treatments or spinal drug pumps implantationand the like. Each possibility is a separate embodiment.

As used herein, the terms “physiological parameter”, “physiologicalfeatures” and “extracted features” may be interchangeably used and mayrefer to at least one or more physiological feature that may beextracted and or derived from at least one physiological signal. Thephysiological parameter may be quantitative or qualitative. According tosome embodiments, the physiological parameter may be derived from thephysiological signal using feature extraction techniques and may includecombining a plurality of extracted features and/or parameters, forexample by non-linear regression techniques. Within the context of thepresent invention the terms “feature extraction”, “feature processing”and “signal processing” may refer to the processes, manipulations andsignal processing measures performed to analyze a physiological signal.Non-limiting examples of suitable physiological parameters are depictedin table 1, below.

TABLE 1 physiological parameters/features Number of features SignalFeature Description 15 ECG Q/R/S/T/P Amplitude, moving average amplitudeand amplitude, variability of amplitude of the Q/R/S/T/P pulse- averageand an array that represent the location and the variability amplitudeof the peak 15 ECG RR/PQ/PR/ The interval, moving average of intervaland QT/RS variability of interval between each pulse or interval,between internal pulse waves, an array that average and represent thelocation of the value computed, as variability the first peak locationand its relevant interval 1 ECG P wave Width of the P wave- an arraythat represent width the location of the P peak and P wave relevantwidth 1 ECG ST level The point of inflection after S wave, which definesbeginning of ST segment. An array that represent the location and pointamplitude. 5 ECG Q,R,S,T,P Derivative of the Amplitude amplitude change5 ECG RR/PQ/PR/ Derivative of the pulses intervals QT/RS interval change1 ECG ST level Derivative of the ST level change 1 ECG QRS widthDerivatives of width of the QRS complex change 1 ECG Energy of Computingthe energy of the residues after ECG applying the spectral cleaning andafter residues applying auto regressive methods 1 ECG Number of Numberof missing R-peaks for a certain time missing R window peaks 4 ECG R-RPower (area) of the VLF, LF, MF and HF Freq. Variability frequenciesanalysis of the interval variability VLF,LF,MF between each pulse in agiven resolution as was and HF defined above in Heart Rate variabilityparagraph 1 ECG R-R Ratio between LF HRV power and HF HRV Freq.Variability power LF/HF 1 ECG RRI Wavelet analysis of the intervalvariability Freq. Variability between each pulse in a given resolution.wavelet analysis 1 ECG alpha Slope of HRV power spectrum Freq. 1 ECGbeta Slope of the log of HRV PS Freq. 3 Respira- Upper peak The peaksvalue, moving average of interval tory values, and variability of peaksamplitude. The peak average, represents the depth of respiration howdeep we variability take a breath. 3 Respira- Lower Peak The lower peaksvalue, moving average of tory values, interval and variability of peaksamplitude. The average, peaks represent the depth of breath release.variability 3 Respira- Respiratory The rate is 1/ Peak to peak distance.The tory rate, interval rate, average rate and variability of theaverage and rate variability 1 Respira- Spectrum Spectrum analysis ofthe respiratory signal tory Analysis of the respiratory 1 Respira- PowerThe area bellow the breath signal tory (area) 6 PPG PPG Peak An arraythat represents location and amplitude and Trough of Peak and Trough.Peak denotes a point of Amplitude, maximum blood volume in a finger;Trough average and denotes a minimum basal blood volume. Bothvariability amplitude, moving average of the amplitude and variabilityare calculated 1 PPG PPG An array that represents location and amplitudemaximum of a point between onset injection and Peak rate point wheremaximum rate of blood volume increase is observed 1 PPG PPG An arraythat represents location and amplitude dicrotic of PPG dicrotic notch.notch 12 PPG PP/TT/NN/ Peak to peak, trough to trough, notch to notch,MM/ maximum rate to maximum rate, and other time intervals, intervalsbetween points of interest in PPG average and beat. Both interval,moving average of the variability interval and variability arecalculated - all representing the pulse rate 12 PPG /PT/PN/NT/ peak totrough, peak to notch, notch to trough, NM notch to maximum rate, andother time intervals, intervals between points of interest in PPGaverage and beat. Both interval, moving average of the variabilityinterval and variability are calculated 5 PPG PP spectral Spectrumanalysis of the Peak to Peak analysis variability: HF, MF, LF and VLFbands power, LF/HF ration 1 PPG Area Under An array that representslocation and integral of Curve single beat of PPG signal (AUC) 1 PPG PPGTime analysis of the envelope of PPG signal. envelope- (envelope -Peak - Trough of PPG signal) time analysis 1 PPG PPG Spectral analysisof the envelope of PPG envelope- signal. (envelope - Peak - Trough ofPPG spectral signal) 1 PPG PPG Wavelet analysis of the intervalvariability Variability between each pulse in a given resolution.wavelet analysis 1 ECG- Respiratory Correlation between the Respirationand the Resp sinus decrease/increase in R-R interval arrhythmia 1 PPG-Respiratory Correlation between the Respiration and the Resp sinusdecrease/increase in PPG intervals arrhythmia 1 ECG- Pulse An array thatrepresent the location and the BP Transition delay between R peak of ECGsignal and Peak time of Blood Pressure signal. (PTT or rPTT) ( Weiss, etal. 1980 ) 1 ECG- Pulse An array that represents the location and thePPG Transition delay between R peak of ECG signal and Peak time of PPGsignal (PTT or rPTT). 1 PPG- Pulse An array that represents the locationand the PPG Transition delay between two PPG signals located on the timesame arteriole in different (PTT or rPTT). 1 CNIBP Average/var- Averageand variability (moving average and iability moving variability) meanaortic pressure (Pmean) 6 CNIBP CBP Peak, An array that representslocation and amplitude and Trough of Peak and Trough. Peak denotes thesystolic amplitude, BP; Trough denotes the diastolic. Amplitude, averageand moving average amplitude and variability are variability calculated1 CNIBP Blood onset An array that represents location and amplitudeejection of a point after Trough where blood ejection is point started(maximum second derivative) 1 CNIBP CBP An array that representslocation and amplitude maximum of a point between onset injection andPeak rate point where maximum rate of blood volume increase is observed(middle of Anacrotic rise) 1 CNIBP CBP An array that represents locationand amplitude dicrotic of CBP dicrotic notch. notch 15 CNIBP PP/PT/PN/Peak to peak, peak to trough, peak to notch, NT/NM notch to trough,notch to maximum rate, and intervals, other time intervals betweenpoints of interest average and in BP beat. variability 1 CNIBP PPspectral Spectrum analysis of the Peak to Peak analysis variability: HF,MF, LF and VLF bands power, LF/HF ration 1 CNIBP Area Under An arraythat represents location and integral of Curve single beat of PPG signal(AUC) 1 CNIBP BP Wavelet analysis of the interval variabilityvariability between each pulse in a given resolution. wavelet analysis 2GSR Average/ Average and variability of perspiration variability (movingaverage and moving variability) Perspiration 1 GSR Peak The timeinterval between peaks, the moving Interval, average of the interval andthe variability average and variability 1 GSR Peak Amplitude, movingaverage amplitude and amplitude, variability of amplitude of the GSRpeaks average, comparing to the base band variability 1 GSR General Thearea under each peak area 1 GSR Phasic The first derivative of the GSRsignal (EDA EDA, phasic), the moving average of the slopes amplitude(normal and absolute values) - mean phasic, average and and thevariability of the slopes variability 1 GSR spontaneous The averagenumber of spontaneous fluctuations fluctuations (SF) in an individualCount 1 GSR Spectral The amplitude of the highest peak in the Analysis:spectrum analysis Peak Amplitude 1 GSR Spectral The frequency of thehighest peak in the Analysis: spectrum analysis Peak Frequency 1 GSRSpectral The power (integration of signal) in the Analysis: differentfrequency and specifically in 0.01- Power 0.04 Hz 1 GSR difference Thedifferences between the values of the between highest peaks in thespectrum analysis of two Peak different locations Amplitude 1 GSR GSRWavelet analysis of the interval variability wavelet between each pulsein a given resolution. analysis 2 Temper- Average/ Average andvariability of perspiration ature variability (moving average and movingvariability) Temperature 1 Temper- Peak The time interval between peaks,the moving ature Interval, average of the interval and the variabilityaverage and variability 1 Temper- Peak Amplitude, moving averageamplitude and ature amplitude, variability of amplitude of thetemperature average, peaks comparing to the base band variability 1Temper- derivative The first derivative of the temperature signal, atureamplitude the moving average of the slopes (normal and average andabsolute values) and the variability of the variability slopes 1 Temper-Spectral The value of the highest peak in the spectrum ature Analysis:analysis Peak Amplitude 1 Temper- Spectral The frequency of the highestpeak in the ature Analysis: spectrum analysis Peak Location 1 Temper-Spectral The power (integration of signal) in 0.01- ature Analysis 0.04Hz Power 1 Temper- Temperature Wavelet analysis of the intervalvariability ature wavelet between each pulse in a given resolution.analysis 2 EOG Average/ variability 4 EEG/ A, β, γ, δ, θ Classical EEGfrequency band definitions. EMG ratio Frequency band Frequency range[Hz] between the delta, δ 0.5-4 - deep sleep (Sometimes is powersreferred as 1-3.5) theta, θ 4-8 - drowsiness (Sometimes is referred as3.5-8) alpha, α 8-14 - relaxed but alert (sometimes is referred as 8-13)beta, β 14-30 - highly alert and focused (sometimes is referred as13-30) gamma γ, 30-70 - represent binding of different populations ofneurons together into a network for the purpose of carrying out acertain cognitive or motor function (sometimes is referred as 36-100) 1EEG/ Average/ EMG variability 1 EEG/ median The frequency at which themedian power is EMG frequency reached 1 EEG/ mean The frequency at whichthe average power is EMG frequency reached 1 EEG/ Mean The average powerof the spectrum within EMG power epoch 1 EEG/ Peak The frequency atwhich the power reaches its EMG frequency peak 1 EEG/ Spectral Thespontaneous EEG frequency below which EMG Edge x percent of the powerare located. Typically x Frequency is in the range 75 to 95. SEF hasvariously been used to estimate the depth of anesthesia. 1 EEG/Approximate For details see (Bruhn, Ropcke and Hoeft EMG Entropy - 2000)1 EEG/ EMG BSR- Burst Suppression ratioThe  burst  suppression  ratio  is  the  proportion  of  the  suppression  EEG  in  the  analyzed  epoch  (usually  one  minute):${BSR} = {\frac{{total}\mspace{14mu}{time}\mspace{14mu}{of}\mspace{14mu}{suppression}}{{epoch}\mspace{14mu}{length}}100\%}$1 EEG/ EMG BcSEF Burst  compensated  spectral  edge  frequency${BcSEF} = {{SEF}\left( {1 - \frac{{BSR}\mspace{14mu}\%}{100\%}} \right)}$1 EEG/ WSMF A generalized form of spectral edge frequency, EMG referredto as weighted spectral median frequency (WSMF), edge frequency iscalculated not necessarily from PSD but from amplitude spectrum, whichis raised to the power p = [0.1 . . . 2.4]; second, the cutofffrequencies of the original spectrum are well- defined; and, third,factor r = [0:05 : : : 0:95] is used, the percentile of the spectrum(e.g., r = 0:5 for MF and r = 0:95 for SEF). 1 EEG/ EMG CUPCanonical  univariate  parameter : frequency  bins  with  a  width  of  3  Hz  or  classical  frequency  bands  are  optimally  weighted  with  the  drugs′  effect-site  concentration  as  obtained  from  pharmacokinetic-pharmacodynamic  (PK-PD)  modeling${CUP} = {\sum\limits_{k = 1}^{10}\;{7_{h}\log\; p_{k}}}$ 1 EEG/ EMGSpEn - Spectral  Entropy${SpEn} = {- {\sum\limits_{k}^{N}\;{p_{k}\log\;{p_{k}.}}}}$ 1 EEG/ EMGBcSpEn - Burst  compensated  Spectral  Entropy${BcSpEn} = {{{SpEn}\left( {1 - \frac{{BSR}(\%)}{100\%}} \right)}.}$ 1EEG/ EMG Beta Ratio${BetaRatio} = {\log\frac{{\hat{P}}_{30 - {47\; H\; z}}}{{\hat{P}}_{11 - 2 - {H\; z}}}}$4 EEG/ Histogram Mean, Standard deviation, Kurtosis, Skewness EMGparameters of signal histogram N EEG/ AR Parameters of AR representation(Schlogl EMG parameters 2006) 3 EEG/ Normalized NSD parameters can bedefined by means of EMG slope first and second derivatives. “Activity”is a descriptors measure of the mean power, “Mobility” is an (Hjorthestimate of the mean frequency and parameters) “Complexity” is anestimate of the bandwidth of the signal (frequency spread) (Hjorth 1973). 3 EEG/ Barlow Parameters based on Barlow EEG model which EMGparameters is an alternative time frequency decomposition. Parameterssuch as Running Mean Frequency and Spectral Purity Index (Goncharova andBarlow 1990) 3 EEG/ Wackermann Three multi-channel linear descriptors ofEEG EMG parameters signal. spatial complexity (Ω), field power (Σ) andfrequency of field changes (Φ) (Wackermann 1999) 1 EEG/ Brain rateWeighted Mean Frequency (Pop-Jordanova EMG and Pop-Jordanov 2005) 1 EEG/EMG SynchFastSlow${{SynchFastSlow} = {\log{\frac{{\hat{B}}_{40 - {47H\; z}}}{{\hat{B}}_{0.5 - {47\; H\; z}}}.{The}}\mspace{14mu}{spectrum}\mspace{14mu}{and}\mspace{14mu}{bispectrum}}},{{derived}\mspace{14mu}{from}\mspace{14mu}{two}\text{-}{second}\mspace{14mu}{epochs}},{{are}\mspace{14mu}{smoothed}\mspace{14mu}{using}\mspace{14mu} a\mspace{14mu}{running}\mspace{14mu}{average}\mspace{14mu}{against}\mspace{14mu}{those}\mspace{14mu}{calculated}\mspace{14mu}{in}\mspace{14mu}{the}\mspace{14mu}{previous}\mspace{14mu}{{minute}.\mspace{14mu} 3}\mspace{20mu}{minutes}\mspace{14mu}{window}\mspace{14mu}{is}\mspace{14mu}{required}\mspace{14mu}{to}\mspace{14mu}{obtain}\mspace{14mu} a\mspace{14mu}{consistent}\mspace{14mu}{estimate}\mspace{14mu}{of}\mspace{14mu}{the}\mspace{14mu}{{bicoherence}.}}$1 EEG/ 80 Hz Ocular microtremor (OMT) is a constant, EOG frequencyphysiological, high frequency (peak 80 Hz), low in EEG near amplitude(estimated circa 150-2500 nm) eye the eyes tremor. 3 EMG Average/Average rectified value (mean of the absolute variability/ windowedsignal) and entropy 1 EMG Spectrum Calculate the power of each frequencyarea - analysis - the location of the EMG should be defined 1 EMG medianThe frequency at which the median power is frequency reached 1 EMG meanThe frequency at which the average power is frequency reached 1 EMG MeanThe average power of the spectrum within power epoch 1 EMG Peak Thefrequency at which the power reaches its frequency peak 1 EMG Mean Theaverage power of the power spectrum power within the epoch 1 EMG Totalpower The sum of the power spectrum within the epoch 1 EMG spontaneousLower oesophageal contractility (LOC). The lower non-striated muscles inthe lower half of oesophageal oesophagus retain their potential activityeven contractions after full skeletal muscle paralysis by (SLOC)neuromuscular blocking agents. Spontaneous lower oesophagealcontractions (SLOC) are non-propulsive spontaneous contractions mediatedvia vagal motor nuclei and reticular activating system in the brainstem. The frequency of these movements is increased as the dose of theanaesthetic is reduced. (Thomas and Evans 1989 ) 1 SVmR Signal SVmR -skin vasomotor reflexes - using laser analysis Doppler 2 Airway Average/End tidal Carbon Dioxid (anesthesia) CO2 Variability 1 Airway AverageEnd tidal sevofluane (anesthesia) Gases 2 Pneumo- Average/ PVR - PulseVolume Recording - plethysmo- Variability Average/Variability ofsignal's amplitude and graph signal analysis N All Change The baselineis computed during the first Signals from the minutes - when the patientis in a constant baseline of position reflecting the position of thetreatment, this patient with no pain stimuli. The differences (distance)of the parameters from this values are calculated (see ‘Normalizationper patient’) N All Cross Cross correlation between all differentsignals - Signals correlation/ canonical correlation Coherence/canonical correlation N All Signature in Signature of a predefinedperiod (for example Signals time - 60 seconds of HR, EEG pattern, orother size of functional defined segment) features 12 accelero- Averageaccelerometer X, Y, Z theta, movement analysis meter value, X,Y,ZVariability θ 1 Environ- Value and ment moving Temper- average ature NEnviron- These features include all patient information ment that mightaffect the level of it stress response parameters on the autonomousnervous system. It will mimic in a certain way the decision system thatis activated, e.g., by the anesthesiologist when deciding when a patientunder anesthesia might suffer from pain 1 Age 1 Gender 1 Weight NDisease Disease e.g. Sympathetic block N Disease For each of the abovediseases define its level Level N Medica- tion N Medicine For each ofthe above medication define its Level level (in mg per day/hour forexample) Evoked Type of evoked pain: heat, cold, electric, pain tetanic,mechanic, pressure, touch, proprioception, chemical or the combinationparamters Level of stimulus and duration Mode of evoked pain: tonic,phasic, Conditioned pain modulation (CPM), repeat, combined Location:painful area, non-painful area, combination SCS On/off, stimulationfrequency, duration, pulse parameters width, intensity, waveform, wavepattern, signal, amplitude, onset timing, delay, treatment length,treatment period, onset delay

As used herein, the term “at least two” with referral to physiologicalparameters may include 2, 3, 4, 5, 6, 7, 8, 9, 10 or more physiologicalparameters. Each possibility is a separate embodiment. According to someembodiments, the at least two parameters may refer to a plurality ofparameters. As used herein, the term “plurality” may refer to 4 or more,5 or more, or 10 or more parameters. Each possibility is a separateembodiment.

As used herein, the term “physiological signal” may refer to anymeasurable signal or event that is measured directly or indirectly froma subject through sensors, transducers or the like. According to someembodiments, the physiological signals may be further analyzed,processed, or otherwise manipulated to provide further details regardingthe state of a patient. According to some embodiment, the physiologicalsignal may be processed to derive physiological parameters.

Non-limiting examples of physiological signals include blood pressure(BP), respiration, internal and/or surface temperature, pupil diameter,galvanic skin response (GSR), and signals received and/or derived fromelectrocardiography (ECG), photoplethysmography (PPG),electrooculography (EOG), electroencephalography (EEG), electromyography(EMG), frontalis electromyogram (FEMG), laser Doppler velocimetry (LDV),dynamic light scattering (DLS), near-infrared spectroscopy (NIRS),partial pressure of carbon dioxide, and accelerometers or any portion orcombination thereof. Preferably a physiological signal may furthercomprise any signal that is measureable and/or detectible from asubject.

According to some embodiments, the identification may be based onparameters extracted from at least PPG and GSR signals and may forexample include PPG amplitude, PPG amplitude variation, pulse rate (PR)interval, PR variability and GSR fluctuations. According to someembodiments, the parameters may be combined using non-linear regression.According to some embodiments, the identification may further be basedon parameters extracted from accelerometer readings.

As used herein, the term “at least one” with referral to physiologicalsignals may include 1, 2, 3, 4, 5 or more physiological signals. Eachpossibility is a separate embodiment.

As used herein, the terms “physiological response” and “physiologicalstatus” may refer to a pattern and/or value obtained for the at leastone physiological signal and/or the at least two physiologicalparameters derived therefrom, for example in response to a stimulusand/or in response to a SCS treatment.

As used herein, the term “Pupil Diameter Measurement (PD)” may refer tomeasurements of pupil size and movement. PD may be measured by infraredvideography or computerized pupillometry.

As used herein, the term “Electromyography (EMG)”, refers to a techniquefor recording and evaluating physiologic properties of muscle activityeither at rest or while contracting. EMG signals may be recorded throughsurface electrodes. A plurality of location specific EMG signals may berecorded from various locations on a subject and/or muscle groups. Forexample Frontalis (scalp) Electromyogram (FEMG) measures the frontalismuscle underlying the forehead.

As used herein, the term “Photo PlethysmoGraph (PPG)” may refer to anon-invasive transducer configured to measure relative changes of bloodvolume from a finger or from other different body locations (finger,hand, earlobe, forehead. forearm, etc.)

As used herein, the term “Electro-Cardio-Gram (ECG)” may refer tonon-invasive recordings of the electrical activity of the heart.

As used herein, the term “ElectroEncephaloGram (EEG)” may refer tonon-invasive readings of the electrical activity of the brain, asrecorded from electrodes placed on the scalp.

As used herein, the term “ElectrogastroenteroGram (EGG)” may refer tonon-invasive readings of the electrical activity of the stomach, and theintestines.

As used herein, the term “Galvanic Skin Response (GSR)” may refer tonon-invasive readings of the electrical conductance or resistance of theskin, which varies depending on the amount of sweat-induced moisture onthe skin. Also known as Skin conductance, electro-dermal response (EDR),skin conductance response (SCR) and Galvanic skin resistance.

As used herein, the term “ElectroOculaGraph (EOG)” may refer tonon-invasive recordings of electrical activity produced by eye movementand retina, as recorded from electrodes placed on the face and frontallobe.

As used herein, the term “Blood pressure (BP)”, may refer to arterialblood pressure, i.e., to the force exerted by circulating blood on thewalls of the larger arteries. BP may be measured by invasive ornon-invasive methods and can be read continuously (Continuous NonInvasive Blood Pressure—CNIBP) or discretely (NIBP).

As used herein, the term “Laser Doppler Velocimetry (LDV)” may refer toquantification of blood flow in tissues such as the skin. LVD may enablecalculation of parameters such as vasomotor reflex (SVMR).

As used herein, the term “Capnography” may refer to measurements ofconcentration or partial pressure of carbon dioxide (CO₂). Othermeasurements on expiratory gases may also be determined for exampleconcentration end-tidal nitrous oxide (N₂O), oxygen (O₂), or anestheticagents.

As used herein, the term “Accelerometer” may refer to a device formeasuring movement, acceleration and gravity induced reaction forces.

According to some embodiments, the at least one stimulus may include: apainful stimulus on non-painful area, painful stimulus on painful area,non-painful stimulus on non-painful area, non-painful stimulus onpainful area. Each possibility is a separate embodiment. According tosome embodiments, the at least one stimulus may include lack ofstimulus.

As used herein, the term “at least one” with referral to stimulus mayinclude 1, 2, 3, 4 or more stimuli. Each possibility is a separateembodiment.

According to some embodiments, the source of the at least one stimulusis selected from tetanic stimulus, thermal (heat or cold) stimulus,pressure stimulus, touch (tickle, itch, crude, flutter, pressure)stimulus, electric stimulus, mechanical stimulus, proprioceptionstimulus, chemical stimulus or combinations thereof. Each possibility isa separate embodiment. According to some embodiments, if more than onestimulus is applied, the stimuli may be of same or different source.

According to some embodiments, the at least one stimulus may be varied.Suitable variations of the at least one stimulus include but are notlimited to: type of stimulation, location of stimulation, duration,intensity, or combinations thereof. Each possibility is a separateembodiment. According to some embodiments, the stimulation may becontrollable and repeatable. According to some embodiments, thestimulation may facilitate classification and evaluation of the efficacyof SCS treatment protocol. According to some embodiments, thestimulation may facilitate classification and evaluation of the efficacyof a nerve stimulation protocol.

According to some embodiments, the painful stimulus and the non-painfulstimulus may be a same source of stimulus applied at a differentintensity. According to some embodiments, the painful stimulus and thenon-painful stimulus may be of a different source.

According to some embodiments, determining the efficacy of the chronicpain treatment, such as SCS treatment, includes determining a level ofpain, before and after the treatment, based on a mathematicalintegration of the at least two physiological parameters obtained inresponse to each of the first and second sets of at least one stimulus.According to some embodiments, the mathematical integration includesapplying linear regression. According to some embodiments, themathematical integration includes applying classification algorithms.

According to some embodiments, applying the classification algorithm mayinclude directly or indirectly comparing the first and secondmeasurements to pre-stored data sets of measurements obtained fromsubjects with known chronic pain treatment efficacies. According to someembodiments, the pre-stored data set includes measurements of the atleast two parameters and/or at least one physiological signal from whichthe at least two parameters can be derived. According to someembodiments, the pre-stored data set includes measurements of the atleast two parameters and/or the at least one physiological signal,obtained prior to and/or after a chronic pain treatment (such as SCStreatment). According to some embodiments, the pre-stored data setincludes measurements of the at least two parameters and/or the at leastone physiological signal, obtained prior to and/or after introduction ofa stimulus, as essentially described herein.

According to some embodiment, applying the classification algorithm mayinclude utilizing a classification technique. Non-limiting examples ofclassification techniques include: Nearest Shrunken Centroids (NSC),Classification and Regression Trees (CART), ID3, C4.5, MultivariateAdditive regression splines (MARS), Multiple additive regression trees(MART), Nearest Centroid (NC) classifier, Shrunken Centroid RegularizedLinear Discriminate and Analysis (SCRLDA), Random Forest, ensembledecision trees, ensemble regression trees, bucket of models, Boosting,Bagging Classifier, AdaBoost, RealAdaBoost, LPBoost, TotalBoost,BrownBoost, MadaBoost, LogitBoost, GentleBoost, RobustBoost, SupportVector Machine (SVM), kernelized SVM, Linear classifier, QuadraticDiscriminant Analysis (QDA) classifier, Naive Bayes Classifier,Generalized Likelihood Ratio Test (GLRT) classifier with plug-inparametric or non-parametric class conditional density estimation,k-nearest neighbor, Radial Base Function (RBF) classifier, MultilayerPerceptron classifier, Bayesian Network (BN) classifier, combinations,ensembles and stacking thereof or any other suitable classificationtechniques known in the art. Each possibility is a separate embodiment.

According to some embodiment, applying the classification algorithm mayinclude using a classifier adept at multiclass classification. Accordingto some embodiment, multi-class classification may be adapted frombinary classifiers as is known and accepted in the art. According tosome embodiment, the binary classifiers may be adapted to perform amulti-class classification by reducing the multi-class problem to aplurality of multiple binary problems using methods as is known andaccepted in the art, for example but not limited to, one-vs-one withvoting schemes by majority vote or pairwise coupling, one-vs-rest, ErrorCorrecting Output Codes, or any combination thereof. Each possibility isa separate embodiment.

According to some embodiments, applying the classification algorithm mayinclude applying a hierarchical multi-class classification. According tosome embodiments, the hierarchical multi-class classification may beperformed as a tree structure having a single parent class. According tosome embodiments, the hierarchical multi-class classification may beperformed as a tree structure having acyclic graph structure with atleast one parental class.

According to some embodiments, applying the classification algorithm mayinclude applying a trained classifier. According to some embodiments,the classifier may be trained on a “training set” such as pre-storeddata sets obtained from subjects with known chronic pain treatmentefficacies, as essentially descried herein. According to someembodiments, the training set may include data for classification thatis made available from publicly available databases, proprietaryclinical trials data, on site recorded data from at least one or moresubject or combinations thereof. Each possibility is a separateembodiment. According to some embodiments, the training set includesinput and output signals that mimic the input and output signals of theclassifier described herein. According to some embodiments, the trainingset includes input signals similar in nature to the expected input, of aclassifier according to the present invention. According to someembodiments, the training set input signals comprise data similar to thephysiological parameters disclosed herein. According to someembodiments, the output signals comprising the training set are similarin nature to the expected output from a classifier, according to thepresent invention. According to some embodiments, the training set iscompiled by a pain expert for example a physician or other skilledperson in the art of pain detection. According to some embodiments, thetraining set is compiled during a clinical trial comprising controlledpain stimuli.

As used herein, the term “learning” and “training” may refer to traininga classifier and/or classifying algorithm on a data set having knowninput (for example physiological parameters and/or combinations ofphysiological parameters) and output values (treatment efficacy), inorder to extrapolate from previously unseen input data an expectedoutput value.

According to some embodiments, applying the classification algorithm mayfurther include taking into consideration demographic data. Non-limitingexamples of demographic data include age, gender, ethnicity, maritalstatus, weight, BMI, height or any other suitable demographic data. Eachpossibility is a separate embodiment.

According to some embodiments, applying the classification algorithm mayfurther include taking into consideration a priori clinical.Non-limiting examples of a priori clinical data include nociceptionresponse to painful stimuli and/or to background chronic pain,conceptual response, context relevance response, behavioral response,subject history, type of prescribed medicine, diagnostics, patientcondition, patient definition of pain level, location of SCS device,drug history, drug interaction or any other clinically relevant data.Each possibility is a separate embodiment. According to someembodiments, including demographic and/or a priori data may increase theclassification efficiency.

According to some embodiments, providing a chronic pain treatment mayinclude providing a trial treatment. As used herein, the term trialtreatment may refer to a short term and/or temporary treatmentconfigured for evaluation of a permanent and/or long term treatment. Asused herein the terms “short term treatment” and “temporary treatment”may include a treatment provided for 6 months or less, 2 months or less,1 month or less, 2 weeks or less, 1 week or less, 3 days or less, 2 daysor less, 1 day or less, or less than one day. Each possibility is aseparate embodiment. As used herein the term “long term treatment” mayinclude a treatment provided for more than 1 month, more than 2 month,or more than 6 months. Each possibility is a separate embodiment.According to some embodiments, the long term treatment may be apermanent treatment.

According to some embodiments, the trial treatment may include proving ashort term treatment with a medicament, such as but not limited to ananalgesic. According to some embodiments, the trial treatment may be atemporary SCS (trial SCS). According to some embodiments, providing atemporary SCS may include implanting into the subject a spinal cordstimulator comprising stimulating electrodes only. According to someembodiments, providing a temporary SCS may include a spinal cordstimulator without implanting an electrical pulse generator.

According to some embodiments, the method may further include predictinga treatment efficacy of a permanent chronic pain treatment, based on thedetermined efficacy of a trial treatment. According to some embodiments,the method may further include providing a treatment recommendationbased on the predicted treatment efficacy. According to some embodimentsthe treatment recommendation may be a recommendation to continue ordiscontinue the chronic pain treatment. For example, the treatmentrecommendation may be to implant a permanent SCS device. For example,the treatment recommendation may be a recommendation not to implant apermanent SCS device. According to some embodiments the treatmentrecommendation may be providing a chronic pain treatment protocol, suchas but not limited to a SCS treatment protocol According to someembodiments, the treatment protocol may include recommended treatmentsettings, such as but not limited to recommended settings of SCSparameters.

According to some embodiments, the method may include predictingefficacy of permanent SCS implantation based on the determined efficacyof a trial SCS. As used herein, the term “permanent SCS” may refer toimplantation of stimulating electrodes together with an electrical pulsegenerator.

According to some embodiments, the chronic pain may include Failed BackSurgery Syndrome (FBSS), complex regional pain syndrome (CRPS),Radiculopathy, Peripheral Vascular Disease (PVD), neuralgia, neuropathicpain, refractory angina pectoris (RAP), Ischemic pain or combinationsthereof. Each possibility is a separate embodiment.

According to some embodiments, there is provided a system fordetermining efficacy of a chronic pain treatment. According to someembodiments, the system includes a processor configured to obtain afirst measurement of at least two physiological parameters in responseto a first set of at least one stimulus; obtain a second measurements ofthe at least two physiological parameters in response to a second set ofthe at least one stimulus and a chronic pain treatment; and determine anefficacy of the chronic pain treatment by applying a classificationalgorithm on the first and second measurements of the at least twophysiological parameters, obtained in response to the first and secondset of at least one stimulus.

According to some embodiments, the chronic pain treatment may be SCStreatment. According to some embodiments, the chronic pain treatment maybe nerve stimulation, such as but not limited to transcutaneouselectrical nerve stimulation (TENS), sacral nerve stimulation or tibialnerve stimulation. Each possibility is a separate embodiment.

According to some embodiments, the system further includes at least onesensor configured to sense (directly or indirectly), at least onephysiological signal. According to some embodiments, the at least twophysiological parameters may be derived from the at least onephysiological signal sensed by the at least one sensor.

As used herein, the term at least one, with referral to sensors mayinclude 1, 2, 3, 4 or more sensors. Each possibility is a separateembodiment. It is thus understood, that the at least two physiologicalparameters may be derived from a same sensor and/or from differentsensors. As a non-limiting example, the at least two physiologicalparameters may include 3 physiological parameters, one parameter derivedfrom an accelerometer and two physiological parameters derived from aPPG sensor.

According to some embodiments, the system may further include a stimulusevoking device configured to provide at least one stimulus to thesubject. Non-limiting examples of suitable stimulus evoking devicesinclude Von Frey Filaments, and Peltier surface stimulator. Eachpossibility is a separate embodiment.

According to some embodiments, the system may further include acommunication module. According to some embodiments, the communicationmodule may be configured to optionally wirelessly communicate thedetermined efficacies of the chronic pain treatment to the subject, acaregiver, and/or to a communication device such as but not limited to amobile telephone, a smartphone, a medical device, a server, a healthcare provider database, a database, a health care provider server, a SCSstimulation device, a nerve stimulation device, or any combinationthereof. Each possibility is a separate embodiment.

According to some embodiments, the system may further include a patientinterface such as but not limited to a keyboard and/or a visual display,or the like.

According to some embodiments, the system may further include a spinalcord stimulator. According to some embodiments, the system may furtherinclude a nerve stimulator.

According to some embodiments, there is provided a method forcalibrating and/or adjusting spinal cord stimulation (SCS) treatment ina subject, the method comprising: providing a SCS being characterized byat least one SCS parameters; varying a value of one of the at least oneSCS parameters along a dynamic range thereof; obtaining measurements ofat least two physiological parameters in response to varying the valueof the one SCS parameter along the dynamic range thereof; determining anefficacy of the SCS along the dynamic range of the varied SCS parameterby applying a classification algorithm to the at least two physiologicalparameters obtained in response to varying the one SCS parameter alongthe dynamic range thereof; and selecting the value of the one SCSparameter yielding the highest efficacy.

According to some embodiments, the method further comprises providing atleast one stimulus to the subject. According to some embodiments, the atleast one stimulus may be provided prior to while and/or after varyingthe SCS parameter along its dynamic range. Each possibility is aseparate embodiment.

According to some embodiments, obtaining measurements of at least twophysiological parameters in response to the SCS, having a SCS parametervaried along its dynamic range, may include obtaining a plurality ofmeasurements each obtained for a different value the varied SCSparameter. As used herein, the term “plurality” may refer to more than 2measurements, such as 3, 4, 5, 10, 100 or more measurements. Eachpossibility is a separate embodiment.

According to some embodiments, obtaining measurements of the at leasttwo physiological parameters while changing the SCS parameter along itsdynamic range may include continuously obtaining measurements of the atleast two physiological parameters, while changing the value of the SCSparameter along its dynamic range. According to some embodiments, theremaining parameters of the at least one SCS parameter may be fixedwhile the one parameter is varied along its dynamic range.

According to some embodiments, the at least one SCS parameter mayinclude type of stimulation, stimulation frequency, duration, pulsewidth, intensity, waveform, wave pattern, signal, amplitude, onsettiming, delay, treatment length, treatment period, onset delay or anycombination thereof. Each possibility is a separate embodiment.

As used herein, the term “dynamic range” with regards to a SCS parametermay refer to any range of values ranging from minimum to maximum of agiven parameter.

According to some embodiments, varying the SCS parameter along itsdynamic range may include making continuous, incremental and/or stepwise changes in the value of the SCS parameter. Each possibility is aseparate embodiment.

As used herein, the term “at least one” with referral to SCS parametersmay include 1, 2, 3, 4 or more SCS parameters. Each possibility is aseparate embodiment.

According to some embodiments, the method may further includedetermining an optimal treatment protocol. According to someembodiments, the optimal treatment protocol may include a treatmentprotocol in which all of the at least one SCS parameter have beenadjusted and/or calibrated to provide a highest efficacy.

According to some embodiments, the method further includes communicatingthe optimal treatment protocol to the subject and/or to a caregiverthereof. According to some embodiments, communicating the optimaltreatment protocol may include wireless communication of the treatmentprotocol. According to some embodiments, the communicating may refer tousing cellular, internet, bluetooth, optical, IR, RF, ultrasound,Near-field communication, or any other suitable communication means.Each possibility is a separate embodiment.

According to some embodiments, there is provided a method forcalibrating and/or adjusting nerve stimulation in a subject, the methodcomprising: providing a nerve stimulation being characterized by atleast one nerve stimulation parameter; varying a value of one nervestimulation parameter along a dynamic range thereof; obtainingmeasurements of at least two physiological parameters in response tovarying the value of the one nerve stimulation parameter along thedynamic range thereof; determining an efficacy of the nerve stimulationalong the dynamic range of the varied nerve stimulation parameter byapplying a classification algorithm to the at least two physiologicalparameters obtained in response to varying the one nerve stimulationparameter along the dynamic range thereof; and selecting the value ofthe one nerve stimulation parameter yielding the highest efficacy.

According to some embodiments, the nerve stimulation may be selectedfrom spinal cord stimulation, transcutaneous electrical nervestimulation (TENS), sacral nerve stimulation or tibial nerve stimulationor combinations thereof. Each possibility is a separate embodiment.

According to some embodiments, the method further comprises providing atleast one stimulus to the subject. According to some embodiments, the atleast one stimulus may be provided prior to while and/or after varyingthe nerve stimulation parameter along its dynamic range. Eachpossibility is a separate embodiment.

According to some embodiments, obtaining measurements of at least twophysiological parameters in response to the nerve stimulation, having anerve stimulation parameter varied along its dynamic range, may includeobtaining a plurality of measurements each obtained for a differentvalue of the varied nerve stimulation parameter. As used herein, theterm “plurality” may refer to more than 2 measurements, such as 3, 4, 5,10, 100 or more measurements. Each possibility is a separate embodiment.

According to some embodiments, obtaining measurements of the at leasttwo physiological parameters while changing the nerve stimulationparameter along its dynamic range may include continuously obtainingmeasurements of the at least two physiological parameters, whilechanging the value of the nerve stimulation parameter along its dynamicrange. According to some embodiments, the remaining parameters of the atleast one nerve stimulation parameters may be fixed while the one nervestimulation parameter is varied along its dynamic range.

According to some embodiments, the at least one nerve stimulationparameter may include type of stimulation, stimulation frequency,duration, pulse width, intensity, waveform, wave pattern, signal,amplitude, onset timing, delay, treatment length, treatment period,onset delay or any combination thereof. Each possibility is a separateembodiment.

As used herein, the term “dynamic range” with regards to a nervestimulation parameter may refer to a range of values ranging fromminimum to maximum of a given parameter.

According to some embodiments, varying the nerve stimulation parameteralong its dynamic range may include making continuous, incrementaland/or step wise changes in the value of the nerve stimulationparameter. Each possibility is a separate embodiment.

As used herein, the term “at least one” with referral to nervestimulation parameters may include 1, 2, 3, 4 or more nerve stimulationparameters. Each possibility is a separate embodiment.

According to some embodiments, the method may further includedetermining an optimal treatment protocol. According to someembodiments, the optimal treatment protocol may include a treatmentprotocol in which all of the at least one nerve stimulation parametershave been adjusted and/or calibrated to provide a highest efficacy.

According to some embodiments, the method further includes communicatingthe optimal treatment protocol to the subject and/or to a caregiverthereof. According to some embodiments, communicating the optimaltreatment protocol may include wireless communication of the treatmentprotocol. According to some embodiments, the communicating may refer tousing cellular, internet, bluetooth, optical, IR, RF, ultrasound,near-field communication, or any other suitable communication means.Each possibility is a separate embodiment.

According to some embodiments, there is provided a system for adjustingand/or calibrating spinal cord stimulation (SCS) in a subject, thesystem comprising a processor configured to receive measurements of atleast two physiological parameters obtained in response to varying a SCSparameter along a dynamic range thereof; determine an efficacy of theSCS along the dynamic range of the varied SCS parameter by applying aclassification algorithm to the at least two physiological parametersobtained in response to varying the one SCS parameter along the dynamicrange thereof; and selecting the value of the one SCS parameter yieldingthe highest efficacy.

According to some embodiments, the processor may further be configuredto control the at least one SCS parameter. According to someembodiments, controlling the at least one SCS parameter may includevarying the parameter along the dynamic range thereof. According to someembodiments, each parameter is varied separately. According to someembodiments, the processor may be configured to automatically vary eachof the at least one SCS parameter along its dynamic range until each ofthe at least one SCS parameters, alone and/or in combination yield ahighest efficacy. According to some embodiments, the processor may beconfigured to automatically vary each of the at least one SCS parameterwhen SCS efficacy reaches a predetermined threshold value.

According to some embodiments, the system may further include acommunication module. According to some embodiments, the communicationmodule may be configured to optionally wirelessly communicate thedetermined efficacies of the chronic pain treatment to the subject, acaregiver, and/or to a communication device, such as but not limited to,a mobile telephone, a smartphone, a medical device, a server, a healthcare provider database, a database, a health care provider server, a SCSstimulation device, or any combination thereof. Each possibility is aseparate embodiment.

According to some embodiments, the system may further include a patientinterface, such as but not limited to, a keyboard and/or a visualdisplay, or the like.

According to some embodiments, the system may further include a SCSdevice.

Reference is now made to FIG. 1A which is a schematic block diagram ofan optional monitoring system 100 for determining an efficacy of spinalcord stimulation (SCS) for a subject with chronic pain.

System 100 includes monitor 110 configured to obtain and analyzemeasurements of at least one physiological signal and to determineefficacy of spinal cord stimulation (SCS) for a subject based on theanalyzed signals. Monitor 110 includes a processing module 112, aclassifier module 114, a sensor module 116, and a communication module118. Monitor 110 may optionally further include an interface module 120.

Processing module 112 preferably comprises a processor and necessarymemory and power supply capabilities for controlling and renderingmonitor 110 functional. Optionally processing module 112 may rendermonitor 110 functional within a cloud computing environment.

Classifier module 114 is configured to provide classification of atleast two physiological parameters derived from the at least onephysiological signal. Preferably classification module 114 facilitatescomputation and processing associated with the classification of pain.Classification module 114 may for example provide for signal processing,normalization, feature extraction and classification. According to someembodiments, classifier module 114 is adept at classifying andidentifying chronic pain that is conducive for spinal cord stimulationtreatment with a spinal cord stimulation device 102, (shown in FIG. 1B).Optionally and preferably module 114 further provides for determiningthe efficacy of SCS treatment in a particular subject. The objectiveevaluation of SCS efficacy and/or the physiological status of thesubject is facilitated and/or based on processing of the at least onephysiological signal with classification module 114. According to someembodiments, classification module 114 enables identifying andclassifying the at least one physiological signal monitored with device110 by utilizing a plurality of processing techniques and classifiers.Optionally the at least one physiological signal is processed andutilized to identify and classify at least two physiological parametersinto classes that identify individuals that may have positive results inpain management with a SCS device 102. Optionally such classificationmay be further processed to provide an evaluation of the efficacy oftreating chronic pain with a SCS device for a particular individual.According to some embodiments, classifier 114 may be an integral part ofprocessor 112.

Sensor module 116 includes at least one sensor configured tonon-invasively sense and record at least one physiological signal from apatient. Suitable sensors and physiological signals have been describedherein. Optionally, the at least one physiological signal may bepre-processed and processed, with classification module 114,facilitating the detection and classification of at least twophysiological parameters and/or a SCS status for example with combinedworking of processing module 112 and classification module 114.Classification related to SCS treatment may be based on a plurality offeatures and/or parameters extracted from at least one physiologicalsignal, for example parameters extracted from PPG and GSR signals andmay include but are not limited to PPG amplitude, PPG amplitudevariation, pulse rate, PR variability and GSR fluctuations. Optionally,the physiological parameters may be combined using non-linear regressionto provide for identifying individuals that may benefit from SCStreatment.

Optionally monitor 110 may further be configured to provide an optimalspinal cord stimulation treatment protocol based on the detection,processing and classification, of the at least one physiological signal.According to some embodiments, the optimal SCS treatment protocol mayinclude optimal settings for at least one SCS parameter, such as but notlimited to, the type of stimulation, stimulation frequency, duration,pulse width, intensity, waveform, wave pattern, signal, amplitude, onsettiming, delay, treatment length, treatment period, onset delay, the likeor any combination thereof.

Communication module 118 is configured to communicate and exchange datawith optional external devices and/or processing units, such as but notlimited to, a display, a computer, a mobile a communication device, amobile telephone, a smartphone, a medical device, a server, a healthcare provider database, a database, a health care provider server, a SCSstimulation device, or any combination thereof. Communication module 118may communicate by any means known in the art, such as but not limitedto, wireless, wired, cellular, internet, Bluetooth, optical, IR, RF,ultrasound, near field communication, the like or any combinationthereof.

Interface module 120 is configured to interfacing with monitor 110.Optimally interface module 120 may be provided in the form of a humaninterface and/or display, such as a keyboard and/or a visual display, orthe like. Optionally interface module 120 may be provided in the form ofa machine to machine interface for example provided in the form of a USBdrive hub, a magnetic scanner, a magnetic card scanner, a memory drives,flash memory drive, volatile memory, non-volatile memory, memory port,mobile memory port and/or drive, or the like. Each possibility is aseparate embodiment.

Monitor 110 may be non-invasively associated with an individualexperiencing chronic pain, where monitoring may be provided by sensormodule 116 and classifier module 114 to identify if the individual maybenefit from SCS treatment. Optionally monitor 110 may be configured toidentify individuals that may benefit from SCS treatment by identifyinga particular set of features extracted from the sensed physiologicalsignals.

Optionally, identifying individuals that may benefit from SCS treatmentusing monitor 110 may further include incorporating offline data forexample a priori data, as essentially described herein. Optionally suchoffline and/or a-priori data may be utilized to facilitateclassification with classifier module 114. Optionally a priori data maybe communicated to monitor 110 via communication module 118 and/or viainterface module 120.

Optionally system 100 may further include or be associated with astimulation evoking device 104 provided for externally stimulating anindividual for example by evoking a physiological response that may thenbe monitored and measured with monitor 110. Stimulation evoking device104 may be configured to evoke a stimulation, such as but not limitedto: a pain evoking stimulation, inert stimulation, a non-pain evokingstimulation, painful stimulation, non-painful stimulation, backgroundpain, tonic stimulation, phasic stimulation, conditioning painmodulation, mechanical induced pain, electric induced pain, tetanicinduced pain, thermal induced pain, proprioception induced pain,chemical induced pain, pressure induced pain, the like or anycombination thereof. Each possibility is a separate embodiment.Optionally, stimulation evoking device 104 may be utilized to providecontrollable and repeatable stimulation that may be varied according toat least one or more parameters for example including, but not limitedto the type of stimulation, location of stimulation, duration,intensity, or the like parameters. Each possibility is a separateembodiment. Optionally stimulation evoking device 104 may be Von FreyFilaments, Peltier surface stimulator, heat or cold thermode, algometer,tetanic stimulator or any other suitable stimulation evoking device.Optionally, stimulation evoking device 104 is configured to providecontrollable and repeatable stimulation which facilitates classificationand evaluation of the efficacy of a SCS treatment protocol using monitor110.

Reference is now made to FIG. 1B showing system 101 comprising monitor110 as previously described with respect to FIG. 1A, and a SCS device102 adapted to provide SCS treatment. Optionally system 100 may furtherinclude stimulation evoking device 104, as described above. SCS device102 may be a standard device that may be provided in the form of atemporary SCS device or an implantable permanent SCS device. OptionallySCS device 102 may be in wired and/or wireless communication withmonitor 110, for example via communication module 118, as described inFIG. 1A above. Optionally, a temporary SCS device may be tested withmonitor 110 to evaluate the efficacy of SCS treatment for an individual,for example as described in further detail in FIG. 2B prior toimplanting a permanent SCS device.

Monitor 110 is configured to detect and/or evaluate the efficacy of aSCS treatment provided by SCS device 102. According to some embodiments,monitor 110 acquires at least one physiological signal from which atleast two physiological parameters are derived, while controlling thestimulation provided with the SCS device 102. Stimulation provided bySCS device 102 may be controlled at various levels, for example ON/OFFstates, as well as settings of stimulation parameters, as essentiallydescribed herein. As a non-limiting example, a subject suffering fromchronic pain may be assessed for his/hers likelihood of benefitting fromSCS. The subject is associated with SCS device 102 and monitor 110,where monitor 110 objectively measures the SCS efficacy and/or enablesanalysis of the level of at least two physiological parameters extractedfrom at least one physiological signal acquired by monitor 110, whileSCS device 102 provides SCS stimulation. Monitor 110 may identify and/orclassify the efficacy of SCS treatment by acquiring and processing atleast one physiological signal obtained from the individual while theSCS device 102 is controllably placed in an ON or OFF state. Forexample, while device SCS 102 is in the ON state, monitor 110 willobjectively measure and/or identify and classify the level of at leasttwo physiological parameters correlated with the ON state. Similarly,while SCS device 102 is in the OFF state, monitor 110 will objectivelymeasure and/or identify and classify the level of at least twophysiological parameters correlated with the OFF state. Monitor 110 maythen enable evaluation the efficacy of SCS treatment based on acomparison of the level of at least two physiological parametersdetermined for the ON and OFF states, respectively.

According to some embodiments, in addition to and/or instead of theON/OFF state evaluation, the level of at least two physiologicalparameters may be determined while controllably alternating stimulationparameters to determine the optimal stimulation required for alleviatingthe pain.

According to some embodiments, monitor 110 provides an efficacyevaluation of SCS following implantation of a temporary SCS device toidentify if an individual is suitable for permanent implantation, asessentially described herein.

According to some embodiments, stimulation evoking device 104 may beutilized to provide controlled and repeatable stimulation, as previouslydescribed. For example, a physiological response pattern of a subjectmay be evaluated by providing stimulation to the subject using painevoking device 104 while SCS device 102 is either in its ON or OFFstate.

Reference is now made to FIG. 2A which is an illustrative flowchart of amethod for determining an efficacy of spinal cord stimulation (SCS) fora subject with chronic pain. The efficacy of SCS may be determinedwithout employing a SCS device, but rather based on acquiring at leastone physiological signal using a monitor such as monitor 110 describedin FIGS. 1A and 1B. Optionally the evaluation process may be facilitatedwith controllable delivery of a stimulus using a stimulation evokingdevice 104, as described in FIG. 1A.

In stage 200, at least one physiological signal of a subject is acquiredusing at least one sensor.

In stage 204, the at least one physiological signal is analyzed andprocessed to extract at least two physiological parameters which arefurther classified using classifier algorithms.

In stage 206, an efficacy of SCS treatment is predicted and/or assessedbased on the classified extracted physiological parameters. Optionallythe results may be communicated to a user or device using communicationmodules, such as communication module 118 or via interface 120, asessentially described in FIGS. 1A and B. It is understood to one ofordinary skill in the art that the results may be presented in variousforms, such as but not limited to, score, percentile, probability or thelike.

Optionally, an additional stage 202, including providing at least onestimulus (pain evoking or non-pain evoking) to the patient may beincluded, as essentially described herein. The subject's reaction to acontrolled and repeatable stimulation (in areas that are susceptible topain as well as areas that are non-susceptible to pain) may furtherallow efficacy evaluation of SCS treatment for the subject beingmonitored.

Reference is now made to FIG. 2B which shows a flowchart of an exemplarymethod for evaluating SCS efficacy, according to some embodiments.

The method depicted in FIG. 2B is similar to that depicted in FIG. 2A,with the additional stage of placement of a temporary and/or permanentSCS device.

In stage 205 at least one physiological signal is acquired and processedto extract at least two physiological parameters which may be furtherclassified using classifying algorithms. According to some embodiments,the processed and classified parameters may be indicative of aphysiological status of the subject.

In stage 210 an SCS device, such as SCS device 102, described in FIG. 1Bare associated with an individual.

In stage 211, the SCS device settings are controllably adjusted in orderto provide an evaluation of SCS treatment for the individual. Preferablythe stimulation provided by SCS device may be controlled at variouslevels for example ON/OFF states and/or settings of SCS parameters.

In stage 212 at least one physiological signal is acquired and processedto extract at least two physiological parameters which may be furtherclassified using classifying algorithms. According to some embodiments,the processed and classified parameters may be indicative of an obtainedSCS efficacy and/or of a physiological status of the subject. The atleast one physiological signal may be acquired and processed for each ofan ON and OFF state of the SCS device. For example, the at least onephysiological signal may be initially acquired and processed when theSCS device is in its ON state and subsequently acquired and processedwhen the SCS device is in its OFF state.

Similarly, the at least one physiological signal may be acquired andprocessed for different SCS parameter settings. For example, loopingbetween stages 212 and 211, as shown with arrow 209 may enable togradually monitor a plurality of SCS parameters, in order to determineoptimal settings for the SCS device.

Optionally in a stage 214 of providing at least one stimulus (painevoking or non-pain evoking) to the subject may be included, asessentially described herein. The subject's reaction to a controlled andrepeatable stimulation (in areas that are susceptible to pain as well asareas that are non-susceptible to pain) may further allow evaluation ofSCS treatment efficacy in alleviating chronic pain.

In stage 216, an evaluation and scoring of the SCS treatment based onthe classification of the physiological parameters that were acquired,processing as described in stages 205 and 212.

Optionally, if efficacy of a temporary SCS is evaluated, stage 218, mayinclude implantation of a permanent SCS device, assuming positiveresults for the evaluation of the temporary SCS device.

Reference is now made to FIG. 3 which depicts a flowchart showing aclosed loop method for controlling an SCS device. The closed loop methodmay be utilized to ensure that an optimal SCS treatment protocol isimplemented.

In stage 300 a SCS device is implanted in a subject suffering fromchronic pain, and a monitoring device configured to monitor at least onephysiological signal, such as monitor 110, is also associated with theuser.

In stage 301, the SCS is configured to a certain SCS treatment programthat is controlled and can be adjusted.

In stage 302, at least one physiological signal is acquired.

In stage 304 the at least one physiological signal is processed toextract at least two physiological parameters which may be furtherclassified using classifying algorithms.

Optionally an additional stage 301 s may be included. Stage 301 sincludes providing at least one stimulus (pain evoking or non-painevoking) to the subject and subsequently evaluating the at least onephysiological signal in response to the controlled and repeatablestimulation (in areas that are susceptible to pain as well as areas thatare non-susceptible to pain), by the methods described herein.

Optionally, in stage 306, a treatment protocol is communicated to theSCS if the SCS is not already programmed or to the interface if manualprogram is required, and to optionally the evoked stimulation,optionally both using a wireless communication link.

In stage 308, an optimal SCS treatment protocol is determined based onthe processed and classified parameters extracted from the physiologicalsignals obtained in stages 302 and 304 as a result of changingstimulation in 301 and 301 s, as essentially described herein.

In stage 310, the SCS device may receive, evaluate and implement theoptimized SCS parameters and protocols, communicated in stage 308. Themethod may now revert to stage 301 for multistage programing.

Reference is now made to FIG. 4 which shows a flowchart of an optionalmethod for detecting and/or evaluating the efficacy of SCS treatment.

In stage 400A a SCS device is set at a first activation mode, referredto as activation mode A, for example SCS device is turned ON.

In stage 402A monitoring device, such as monitoring device 110, isprovided and at least one physiological signal is acquired. The at leastone physiological signal is processed to extract at least twophysiological parameters which are further analyzed using a classifieralgorithm to allow classification of a physiological status and/or SCSefficacy of the subject when the SCS device is in activation mode A, forexample ON.

In stage 400B the SCS device is set at a second activation mode,referred to as activation mode B, for example SCS device is turned OFF.

In stage 402B the at least one physiological signal is acquired andprocessed to extract at least two physiological parameters which arefurther analyzed using the classifier algorithm to allow classificationof a physiological status and/or SCS efficacy of the subject when theSCS device is in activation mode B, for example OFF.

Optionally activation mode A and B are alternate forms of the samestimulation parameter or device states. For example, if activation modeA is the ON state then activation mode B is the device OFF state.Additionally or alternatively, the activation modes A and B may refer todifferent values of a SCS parameter, such as but not limited to SCSfrequency. Optionally a loop between stages 400A, 402A, 400B and 402Bmay be used such that all (or some) optional combinations of treatmentparameters and protocols may be analyzed.

In stage 404 based on the monitoring results provided in stages 400A/B,402A/B, a predication and/or evaluation of the SCS treatment efficacymay be provided.

EXAMPLES Example 1: Radicular Pain

33 patients with chronic radicular (neuropathic) pain in one lowerextremity and an implanted permanent spinal cord stimulator (SCS)participated in the study.

Patients were tested twice in a random order: first for 30 minutes afterturning the SCS ON, and two hours after turning it OFF.

Patients were requested to rate the intensity of their radicular pain onnumerical pain scale (NPS, 0-100) twice, at a random order: thirtyminutes after turning the SCS on and two hours after turning it off. Forthe purpose of this study, a difference of 15 NPS points or more betweenthe two ratings (stimulator “on” and “off”) was regarded as an“effective SCS”.

Plethysmogram (PPG) and Galvanic Skin Response (GSR) through the skinconductance were acquired for 120 seconds, using a monitoring device[PMD-100™ (Medasense, Israel)], to extract the following autonomicparameters: PPG amplitude, PPG amplitude variation, pulse rate (PR)interval, PR variability and GSR fluctuations.

The parameters were combined using a non-linear classifier. Theaccuracy, sensitivity, specificity, positive and negative predictedvalues (PPV and NPV) of each parameter alone and their combination werecalculated. Paired t-test was used for statistical analyses.

Results: Effective SCS was found in 18 patients and ineffective in 15patients (ΔNPS: 40±17 vs. 8.5±7, respectively; p<0.05). The combinationof the parameters outperformed each of the parameters alone in thedetection of the SCS effectiveness, with regards to all classificationperformance criteria (accuracy of the combination was 85% and that ofeach parameter ranged from 52% to 70%; sensitivity 94% vs 53-82%;specificity 75% vs 38-73%; PPV 76% vs 64-76%; NPV 92% vs 46-79%).

Conclusions: This shows that autonomic-based multi-parameter assessmentprovided by the monitor disclosed herein may be used for objectivemeasurement of SCS effectiveness.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting. As used herein, thesingular forms “a”, “an” and “the” are intended to include the pluralforms as well, unless the context clearly indicates otherwise. It willbe further understood that the terms “comprises” or “comprising”, whenused in this specification, specify the presence of stated features,integers, steps, operations, elements, or components, but do notpreclude or rule out the presence or addition of one or more otherfeatures, integers, steps, operations, elements, components, or groupsthereof. Unless otherwise defined, all technical and scientific termsused herein have the same meaning as commonly understood by one ofordinary skill in the art to which this invention belongs.

Unless specifically stated otherwise, as apparent from the followingdiscussions, it is appreciated that throughout the specificationdiscussions utilizing terms such as “processing”, “computing”,“calculating”, “determining”, “estimating”, or the like, refer to theaction and/or processes of a computer or computing system, or similarelectronic computing device, that manipulate and/or transform datarepresented as physical, such as electronic, quantities within thecomputing system's registers and/or memories into other data similarlyrepresented as physical quantities within the computing system'smemories, registers or other such information storage, transmission ordisplay devices.

Embodiments of the present invention may include apparatuses forperforming the operations herein. This apparatus may be speciallyconstructed for the desired purposes, or it may comprise a generalpurpose computer selectively activated or reconfigured by a computerprogram stored in the computer. Such a computer program may be stored ina computer readable storage medium, such as, but is not limited to, anytype of disk including floppy disks, optical disks, CD-ROMs,magnetic-optical disks, read-only memories (ROMs), random accessmemories (RAMs) electrically programmable read-only memories (EPROMs),electrically erasable and programmable read only memories (EEPROMs),magnetic or optical cards, or any other type of media suitable forstoring electronic instructions, and capable of being coupled to acomputer system bus.

The processes and displays presented herein are not inherently relatedto any particular computer or other apparatus. Various general purposesystems may be used with programs in accordance with the teachingsherein, or it may prove convenient to construct a more specializedapparatus to perform the desired method. The desired structure for avariety of these systems will appear from the description below. Inaddition, embodiments of the present invention are not described withreference to any particular programming language. It will be appreciatedthat a variety of programming languages may be used to implement theteachings of the inventions as described herein.

The invention may be described in the general context ofcomputer-executable instructions, such as program modules, beingexecuted by a computer. Generally, program modules include routines,programs, objects, components, data structures, and so forth, whichperform particular tasks or implement particular abstract data types.The invention may also be practiced in distributed computingenvironments where tasks are performed by remote processing devices thatare linked through a communications network. In a distributed computingenvironment, program modules may be located in both local and remotecomputer storage media including memory storage devices.

While a number of exemplary aspects and embodiments have been discussedabove, those of skill in the art will recognize certain modifications,additions and sub-combinations thereof. It is therefore intended thatthe following appended claims and claims hereafter introduced beinterpreted to include all such modifications, additions andsub-combinations as are within their true spirit and scope.

The invention claimed is:
 1. A system for SCS treatment efficacyassessment, the system comprising a processing module, a sensor moduleand a classifier module; wherein said processing module is configuredto: receive a first indication from a stimulus evoking device that afirst set of at least one stimulus is being provided; initiate saidsensor module to conduct a first measurement of at least onephysiological signal in response to the received first indication,wherein said sensor module comprises at least one sensor configured tonon-invasively sense the at least one physiological signal of a subject;receive an indication from a SCS device that a SCS treatment is beingprovided; receive a second indication from the stimulus evoking devicethat a second set of the at least one stimulus is being provided;initiate said sensor module to conduct a second measurement of the atleast one physiological signal in response to the second indication fromthe stimulus evoking device and the indication from the SCS device; andinitiate said classifier module to, by a non-transitory computerprogram, extract at least two features from each of said at least onephysiological signals obtained from the first and second measurements;and classify the at least two features by applying a classificationalgorithm thereon, wherein the at least one physiological signalcomprises a photoplethysmograph (PPG) signal and/or a galvanic skinresponse (GSR) signal and wherein the at least two features comprise PPGamplitude, PPG amplitude variation, pulse rate (PR) interval, PRvariability, GSR Amplitude, GSR fluctuations or any combination thereof.2. The system of claim 1, further comprising a stimulus evoking deviceconfigured to provide the at least one stimulus to the subject.
 3. Thesystem of claim 1, further comprising a SCS device.
 4. The system ofclaim 3, wherein the SCS device comprises a temporary spinal cordstimulator without an electrical pulse generator.
 5. The system of claim1, wherein applying the classification algorithm further comprisesdirectly or indirectly comparing the first and second measurements topre-stored data sets of measurements obtained from subjects with knownSCS treatment efficacies.
 6. The system of claim 1, wherein an efficacyscore for the SCS treatment is further based on patient demographicdata.
 7. The system of claim 6, wherein the efficacy score of the trialSCS treatment is indicative of long-term SCS treatment efficacy.
 8. Thesystem of claim 7, wherein said processor is further configured toadvise if implantation of a permanent spinal cord stimulator isrecommended based on the predicted treatment efficacy.
 9. The system ofclaim 1, wherein the at least one stimulus is selected from: painfulstimulus on non-painful area, painful stimulus on painful area,non-painful stimulus on non-painful area, non-painful stimulus onpainful area.
 10. The system of claim 1, wherein the at least onestimulus is selected from tetanic stimulus, thermal stimulus, pressurestimulus, touch stimulus, electric stimulus, mechanical stimulus,proprioception stimulus or combinations thereof.
 11. The system of claim1, wherein the first and second stimulus are same or different.
 12. Thesystem of claim 1, wherein the at least one physiological signal furthercomprises an electrocardiogram (ECG) signal, a blood pressure signal, arespiration signal, an internal body temperature signal, a skintemperature signal, a electrooculography (EOG) signal, a pupil diametersignal, a electroencephalogram (EEG) signal, a frontalis electromyogram(FEMG) signal, a electromyography (EMG) signal, an electro-gastro-gram(EGG) signal, a laser Doppler velocimetry (LDV) signal, a dynamic lightscattering (DLS) signal, a near-infrared spectroscopy (NIRS) signal, apartial pressure of carbon dioxide signal, or an accelerometer reading.13. The system of claim 12, wherein the at least two features furthercomprise PPG Peak (P) amplitude, mean PPG Peak (P) amplitude, standarddeviation (std) of PPG Peak (P) amplitude, Trough (T) amplitude, meanTrough (T) amplitude, std of Trough (T) amplitude; PPG dicrotic notch(N) amplitude, mean dicrotic notch (N) amplitude, std of dicrotic notch(N) amplitude, PPG peak to peak time intervals, PPG peak to peakinterval mean, PPG peak to peak interval std; power spectrum of the PPGpeak to peak intervals: VLF Power, LF Power and HF Power; GSR amplitude,GSR mean amplitude, GSR amplitude std; GSR Peak (P) amplitude, mean Peak(P) amplitude and Peak (P) amplitude std; GSR peak to peak timeintervals, mean GSR peak to peak time interval; GSR peak to peak timeintervals std; Phasic EDA: amplitude, mean amplitude and std ofamplitude, Temperature amplitude, mean amplitude and std of amplitude;Temp Peak (P) amplitude, mean amplitude and std of amplitude;Temperature peak to peak time intervals, mean and std (variability) ofinterval; ECG to PPG Pulse Transition time; PPG to PPG Pulse Transitiontime; ECG R to R time intervals, mean and std (variability) ofintervals; Power of VLF, LF and HF frequency bands of power spectrum ofthe ECG R to R intervals (heart rate variability); Upper peak amplitude,mean amplitude and STD of amplitude; Respiratory rate, mean rate and stdrate; Power of the frequency bands of power spectrum of EMG signal; EMGPower Spectrum Mean frequency; EMG Power Spectrum Highest PeakFrequency; Power of the alpha, beta, gamma, delta, theta frequency bandsof power spectrum of EEG/FEMG signal; EMG Power Spectrum Mean frequency;EMG Power Spectral edge frequency; Coherence between 2 or more EEG/FEMGchannels; frequency of movement; axis of movement; or any combinationthereof.
 14. The system of claim 1, wherein the at least twophysiological features comprise at least three physiological features.15. The system of claim 1, wherein, wherein said processing module andsaid classifying module are integrated into a single unit.